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Időpont: 2017. május 25-én (csütörtök), 14:15 órakor a H épület 306-os teremben


Philipp Hungerlaender and Franz Rendl  Alpen-Adria Universitaet Klagenfurt

Active set methods and the semismooth Newton method for convex quadratic programming


The semismooth Newton method of Kunisch et al for bound constrained convex quadratic programming is extremely efficient, if it converges.Unfortunately, global convergence may fail in general.

We first present two variants to make it globally convergent, one uses recursion, the other a type of combinatorial line search. Both variants maintain the positive features of the SN-Method, and there does not seem to be a clear champion among the two.

Finally, we address modifications to make the SN-Method applicable to general convex quadratic problems, including linear equality constraints. First computational experiments look very encouraging.